Iterative Combinatorial Auctions: Theory and Practice
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Virtual worlds: fast and strategyproof auctions for dynamic resource allocation
Proceedings of the 4th ACM conference on Electronic commerce
Combinatorial Auctions: A Survey
INFORMS Journal on Computing
Brain Meets Brawn: Why Grid and Agents Need Each Other
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Dynamic Resource Prices in a Combinatorial Grid System
CEC-EEE '06 Proceedings of the The 8th IEEE International Conference on E-Commerce Technology and The 3rd IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services
Dynamic Pricing and Automated Resource Allocation for Complex Information Services: Reinforcement Learning and Combinatorial Auctions (Lecture Notes in Economics and Mathematical Systems)
A New and Improved Design for Multiobject Iterative Auctions
Management Science
Mirage: a microeconomic resource allocation system for sensornet testbeds
EmNets '05 Proceedings of the 2nd IEEE workshop on Embedded Networked Sensors
Taming the computational complexity of combinatorial auctions: optimal and approximate approaches
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
A q-learning based adaptive bidding strategy in combinatorial auctions
Proceedings of the 11th International Conference on Electronic Commerce
An adaptive bidding strategy for combinatorial auction-based resource allocation in dynamic markets
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
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In this article we present an agent-based simulation environment for task scheduling in a grid-like computer system. The scheduler allows to one simultaneously allocate resources such as CPU time, communication bandwidth, volatile and non-volatile memory by employing a combinatorial resource allocation mechanism. The allocation is performed by an iterative combinatorial auction in which proxy-bidding agents try to acquire their desired resource allocation profiles with respect to limited monetary budget endowments. To achieve an efficient allocation process, the auctioneer provides resource price information to the bidders. We use a pricing mechanism based on shadow prices in a closed loop system in which the agents use monetary units awarded for the resources they provide to the system for the acquisition of complementary capacity. Our objective is to identify optimal bidding strategies in the multi-agent setting with respect to varying preferences in terms of resource quantity and waiting time for the resources. Based on a utility function we characterize two types of agents: a quantity maximizing agent with a low preference for fast bid acceptance and an impatient bidding agent with a high valuation of fast access to the resources. By evaluating different strategies with varying initial bid pricing and price increments, it turns out that for quantity maximizing agents patience and low initial bids pay off, whereas impatient agents should avoid high initial bid prices.